Working time of different processes (<i>h</i>). by Dawei Wang (471687)

A Solvent Selection Framework for Porous Organic Polymers by Xue Fang (648169)

“…In this work, we present a solvent selection toolkit for functional porous organic polymers. We have developed the <i>MLoc</i> algorithm for the fast determination of Hansen solubility parameters (HSPs) for novel materials. …”

Data_Sheet_1_Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics.pdf by Mike Diessner (14236922)

Integration of multiple models through evolutionary multiobjective optimization improves RfaH sequence design outcomes. by Lu Hong (399882)

“…<p>Each column represents one genetic algorithm setup, indicated by the legend above each column that describes how, at each iteration, the subset of designable positions is selected, how the mutations are proposed, and which objective functions are used to score the designs; here, RND stands for random, pMPNN stands for ProteinMPNN, and ESM refers to ESM-1v. …”

Akt1 prodigiosin docking and dynamic molecular by Fares ELGHALI (18467655)

“…</p><p dir="ltr"><b>Molecular dynamic simulations</b></p><p dir="ltr">The molecular dynamics simulation was conducted to explore the dynamic interactions between the ligand and Akt1 protein complex. Following the methodology outlined by Faker et al. 2024,(Frikha et al., 2024) Here's a breakdown of the steps outlined: (1) Preparation of Inputs: The CHARMM-GUI was used to prepare inputs with the CHARMM36 force field for molecular simulations. (2) System Setup: The initial structure was placed in a rectangular box, solvated with water molecules using the TIP3P model, and neutralized by adding potassium (K+) and chloride (Cl-) ions. (3) Energy Minimization: The system underwent energy minimization using the steepest descent energy minimization algorithm for 50,000 steps to relieve steric clashes and correct bad contacts. (4) Pre-Equilibration Simulation: A pre-equilibration simulation was conducted for 125 picoseconds (ps) in the NVT ensemble (constant number of particles, volume, and temperature) at 300 Kelvin (K) using velocity rescaling with a stochastic term. (5) NPT Equilibrium Simulation: Each system underwent an NPT equilibrium simulation for 125 ps in the NPT ensemble (constant number of particles, pressure, and temperature). …”

Tuning the only free parameter to match empirical results. by José Camacho Mateu (11473518)

Scenario 1. by Ayyappan Subramaniam (16679704)

S1 File - by Ayyappan Subramaniam (16679704)

Scenario 1. by Ayyappan Subramaniam (16679704)

Scenario 1. by Ayyappan Subramaniam (16679704)

Reserve cost Scenario 1. by Ayyappan Subramaniam (16679704)

table1_Clustering of Experimental Seismo-Acoustic Events Using Self-Organizing Map (SOM).docx by Flora Giudicepietro (10043249)

“…We parameterized the seismo-acoustic events in the frequency domain by applying the Linear Predictive Coding to both accelerometric and acoustic signals generated by the dynamics of various degassing regimes, and in the time domain, applying a waveform function. …”

Schematic representation of the simulated conditions. by Inês G. Gonçalves (8924393)

“…Unlike Setup 1, cell death and proliferation were considered in this setup. …”

Influence of weight decay on model performance. by Ulysse Rançon (15451568)

“…We report here results of control models on the NS1 dataset. The table presents test performances as a function of the weight decay parameter used with AdamW optimizer in our setup. …”

A New Control Approach for a Multi-Controlled Wheelchair Utilizing Deep Learning Framework and Raspberry Pi by Sudipta Chatterjee (536333)

“…Employing a deep learning algorithm, it performs real-time object detection and tracks its current position in various environmental conditions. …”